Techniques of detecting anomalies in time series data have been studied in recent years. The main purpose of anomaly detection in time series is to locate events and time instances where irregular actions or patterns occur. Depending on the specifications of different applications, several definitions for anomaly have been proposed. For example, discord in time series can include subsequences (with a predefined length) that are maximally different to all of the rest of the time series subsequences. A symbolic aggregate approximation (SAX) technique has been used to locate such subsequences. As another example, an anomaly can be considered as a sufficiently infrequent time series pattern. A pattern can be considered to be sufficiently infrequent or surprising if the frequency of its occurrences differs substantially from what is expected according to previous experience. Linear characteristics of suffix trees have been used to extract such patterns from long time series.
Despite the progress made to date, current techniques of anomaly detection suffer from certain deficiencies. In particular, current techniques typically rely upon a priori information about time sequences in order to detect anomalies. Also, the deficiency of current techniques has been reported when such techniques are applied to time series data collected from wearable sensors.
It is against this background that a need arose to develop the apparatus, system, and method described herein.